Timing of Outcomes and Expectations After Knee Surgery in the US Military: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Knee injury and subsequent surgery are widespread in the military setting. Associations between knee surgery and expected outcomes over time have not been consolidated and characterized systematically by procedure type across the body of literature, and the temporal expectations of these outcomes remain unclear. OBJECTIVE: To summarize common postoperative follow-up times and associated outcomes that determine clinical or surgical failure in US service members after elective knee surgery. DATA SOURCES: A systematic search was conducted with 3 bibliographic databases of published research reports from 2010 through 2021. STUDY SELECTION: Studies in US military service members undergoing elective knee surgery, with a minimum of 1-year follow-up, and reporting on a functional/occupational outcome were included. Three reviewers screened all abstracts and full-text articles to determine eligibility. STUDY DESIGN: Systematic review of longitudinal cohort studies. LEVEL OF EVIDENCE: Level 2a. DATA EXTRACTION: Extracted data included military demographics, surgical procedure variables, surveillance period, and outcome measures. The Newcastle-Ottawa Scale and the Grading of Recommendations Assessment, Development, and Evaluation approach were used to determine study quality and risk of bias. RESULTS: A total of 22 studies (mean follow-up time of 40.7 months) met the inclusion criteria. For cruciate ligament repair, approximately one-third of patients required a second surgery or were medically separated from military service by 2 years from surgery; 100% were reinjured by 4 years, and 85% sustained a new injury within 5 years of surgery. For meniscal repair, nearly one-third of patients were medically separated, and half were placed on activity restrictions within 3 years of surgery. For articular cartilage repair, within 5 years, 39% of patients required a second surgery, 30% were placed on activity restrictions, and 36% were medically separated. For patellar repair, 37% of patients were medically separated and over half were placed on activity restrictions within 5 years. CONCLUSION: Common knee surgeries can have long-term implications for military careers that may not become apparent with shorter follow-up periods (<2 years). When longer surveillance periods are used (eg, up to 5 years), additional surgical procedures are more common and the likelihood of being injured or medically separated from military service is higher.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".